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 Table of Contents  
ORIGINAL ARTICLE
Year : 2022  |  Volume : 29  |  Issue : 3  |  Page : 214-220

Frequency of kidney dysfunction in patients with acute stroke and the relationship with the type, severity and outcome


1 Department of Internal Medicine, Faculty of Clinical Science, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
2 Department of Medicine, Bowen University Teaching Hospital, Ogbomoso, Oyo State, Nigeria

Date of Submission09-Feb-2022
Date of Decision25-Mar-2022
Date of Acceptance07-Jun-2022
Date of Web Publication22-Jul-2022

Correspondence Address:
Abdul-Karim Olayinka Shitu
Department of Medicine, Faculty of Clinical Science, Ladoke Akintola University of Technology, Ogbomoso, Oyo State
Nigeria
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/npmj.npmj_34_22

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  Abstract 


Background/Aim: Kidney dysfunction is an established risk factor for cardiovascular diseases including stroke. The study aimed at assessing the frequency of kidney dysfunction in patients with acute stroke and to evaluate the relationship to the type, severity and outcome of stroke. To establish a relationship, which has not been explained in past studies. Materials and Methods: This was a cross-sectional analytical study on acute stroke patients and matched controls, evaluating for kidney dysfunction using both estimated glomerular filtration rate (GFR) and the spot urine protein creatinine ratio. The type of stroke was observed by neuroimaging. The National Institute of Health Stroke Score was used to assess the severity of stroke at presentation and outcome after 7 days. Data analysis was done using Statistical Package for Social Sciences (SPSS) application version 23.0 (SPSS Inc., Chicago, IL, USA). Results: Ninety-eight patients and 100 controls were recruited, with a mean age of 64.7 ± 15.5 and 64.8 ± 15.1 years, respectively. The patients with stroke had a statistically significant higher frequency of kidney dysfunction compared to the controls (85.9% vs. 62.0%, P ≤ 0.001). Patients with haemorrhagic stroke had a higher frequency of kidney dysfunction compared with those with ischaemic stroke (93.8% vs. 77.3%, P = 0.048). The proportion of patients with kidney dysfunction was seen to increase from those with mild to those with severe stroke symptoms, both at presentation and after 7 days. Estimated GFR was seen to be an independent predictor of poor outcome in patients with stroke (odds ratio 0.955, 95% confidence interval 0.924 – 0.986, P = 0.005). Conclusion: The study demonstrated that in patients with acute stroke there is a high frequency of kidney dysfunction. Haemorrhagic stroke, increasing stroke severity and poor outcome were seen to be associated with kidney dysfunction. Thus, recommending the need for kidney care as an important part of stroke management.

Keywords: Estimated glomerular filtration rate, kidney dysfunction, proteinuria, stroke, urine protein creatinine ratio


How to cite this article:
Shitu AKO, Akinsola A, Ayodele OE, Bademosi OF. Frequency of kidney dysfunction in patients with acute stroke and the relationship with the type, severity and outcome. Niger Postgrad Med J 2022;29:214-20

How to cite this URL:
Shitu AKO, Akinsola A, Ayodele OE, Bademosi OF. Frequency of kidney dysfunction in patients with acute stroke and the relationship with the type, severity and outcome. Niger Postgrad Med J [serial online] 2022 [cited 2022 Aug 9];29:214-20. Available from: https://www.npmj.org/text.asp?2022/29/3/214/351723




  Introduction Top


The prevalence of kidney dysfunction has been estimated to be between 10% and 20% of the adult population in most countries worldwide.[1] Globally in 2017, kidney dysfunction resulted in 61.3 million disability-adjusted life years (DALYs), of which 41.6% were caused by cardiovascular disease (CVD) attributed to kidney dysfunction, with 40.2% of these CVD being caused by stroke.[2] The prevalence of CKD in Nigeria as shown by various studies ranges between 8% and 26.1%.[3],[4],[5]

The global all-age prevalence of kidney dysfunction in the form of chronic kidney disease (CKD) worldwide has risen by 29.3% between 1990 and 2017.[2] It is projected that CKD will rank 5th in total years of life lost by 2040.[6] The prevalence of CKD in Nigeria has increased by 7.8% between 1990 and 2017.[2] According to the Kidney Disease Outcome Quality Initiative, cardiovascular events are the leading causes of morbidity and mortality in CKD patients.[7]

In 2019, stroke was ranked as the 3rd leading cause of DALYs at all ages, worldwide.[8] Between 1990 and 2019, the incidence of stroke across Nigeria ranged from 114.0 to 158.0 per 100,000.[9],[10] Cerebrovascular disease is more prevalent in patients with CKD than in the general population and both CKD and CVD share traditional cardiovascular risk factors.[11] This relationship has been demonstrated in Nigeria, as shown in a hospital-based study by Lawal et al.[12]

Glomerular filtration rate (GFR) is generally accepted as the best overall index of kidney function, and the presence of proteinuria is a marker of kidney damage.[7] Estimated GFR (eGFR) and proteinuria which are markers of kidney dysfunction have been shown to impact the course of a stroke.[13],[14]

Kidney function among patients with stroke has been studied in developed countries in hospital-based studies.[15],[16],[17] However, the relationship between kidney dysfunction and stroke has been less well investigated in Africa and, particularly in Nigeria, despite the high incidence of stroke and kidney disease.[2],[10]

This study determined the frequency of impaired kidney function in patients with acute stroke in Ogbomoso, Nigeria, using eGFR and proteinuria as markers of kidney dysfunction; and also evaluated the impact of kidney dysfunction on the type, severity and short-term outcomes of patients with stroke.


  Materials and Methods Top


The study was a cross-sectional analytical study carried out on patients with newly diagnosed acute stroke, presenting within 72 h of the onset of symptoms through the Accident and Emergency Department and Neurology Clinic of Bowen University Teaching Hospital (BUTH), Ogbomoso, Oyo State, South-West Nigeria.

The Ethical Committee of BUTH gave ethical approval for the study (Registration Number: NHREC/12/04/2012; Approval Number: BUTH/REC-109; Dated: 10/01/2018).

Beneth's formula was used to determine the sample size:[18]



Where,

N = desired sample size (for samples more than 10,000)

Zi-a = standard deviation (SD) set at 1.96 which corresponds to a 95% confidence level.

p = estimated prevalence in the target population (crude prevalence of stroke = 58/100000).[19]

d = absolute precision, i.e., the value (in percentage points) that indicates the maximum difference between the population rate and the sample rate that can be tolerated.[18] Ten per cent (0.1) was assumed for this study.



N = 93.5

Allowing for a 10% attrition rate, 102 patients were recruited for the study. A corresponding 102 age- and sex-matched controls were also recruited.

All patients ≥18 years with a clinical diagnosis of stroke as defined by the World Health Organization monitoring trends and determinants of cardiovascular disease (MONICA) study,[20] who presented within 72 h of onset of symptoms and consented to participate in the study. Patients were recruited consecutively until the sample size of 102 was obtained. A corresponding number of age- and sex-matched apparently normal controls were also recruited as well. Participants were recruited over 1 year period, from September 2018 to August 2019.

A semi-structured questionnaire was used as the survey instrument to collect information about the subjects' and controls' socio-demographic characteristics, risk factors for stroke, history suggestive of kidney disease and assessment of the stroke. The National Institute of Health Stroke Scale (NIHSS)[21] was administered within 24 h of presentation and on the 7th day of admission to determine the severity of stroke. The poor outcome measure was defined as <8 point improvement in the NIHSS score by the 7th day of admission or death on or before the 7th day.[22] The type of stroke was determined by neuroimaging (either cranial computer tomography scan or magnetic resonance imaging, as indicated for the patient's management).

All participants had their blood pressure assessed according to recommendations of the American Heart Association Council on Hypertension.[23] Blood samples were collected for determination of serum electrolyte, urea and creatinine, fasting blood glucose, fasting lipid profile, all determined by colorimetric technique. Urine was collected to determine spot urine protein creatinine ratio (uPCR) using a Beckman Coulter Clinical Chemistry Analyser (Model: AU680, Serial Number: 2013082527, Manufactured in Japan, in 2013).

The creatinine-based eGFR was calculated using the CKD Epidemiology Collaboration (CKD-EPI) equation.[24] In this study, kidney dysfunction was defined as an eGFR of <60 mL/min/1.73 m2 and/ or an uPCR of >150 mg/g. Other operational definitions for the variables in the study are stated in [Table 1].
Table 1: Operational definitions of medical history, clinical and laboratory findings

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Data were analysed using Statistical Package for Social Sciences (SPSS) version 23 (SPSS Chicago Inc.; IL, USA) after data entry into the software package. Numerical variables were assessed using distribution (Q-Q) plots, parametric (normally distributed) data were presented as means ± SD and non-parametric data as medians with interquartile range. Categorical variables were presented as percentages. The difference between two means was assessed by employing the Student's t-test for parametric data and the Mann–Whitney U test for comparing non-parametric data. The Chi-square was used to assess the degree of association of categorical variables with Fischer's exact correction applied as appropriate. The presence of kidney dysfunction was compared with the frequency of the type of stroke, and the severity of stroke using the NIHSS scores at presentation and on the 7th day using the Chi-square test. Logistic regression analysis was used to determine multivariate-adjusted odds ratios (ORs) with 95% confidence intervals (CIs) for the study outcomes, of which the model included the age, blood pressure, lipid profile, fasting blood glucose, type of stroke and NIHSS at presentation as confounding factors. Statistical significance was taken as P < 0.05.


  Results Top


One hundred and two patients with stroke were recruited for the study. However, two patients opted out after being recruited. Another two patients had analytical errors with their spot urine protein-creatinine ratio. Thus, the data of 98 patients were analysed for this study. The control group consisted of 100 age- and sex-matched participants.

[Table 2] shows the sociodemographic characteristics of the study participants. There were 50 (51%) males and 48 (49%) females among the 98 patients recruited. The mean age (±SD) of the patients was comparable to the control group (64.7 ± 15.5 vs. 64.8 ± 15.1 years, P = 0.946). There was no statistically significant difference in the educational status of the patients and the controls.
Table 2: Baseline demographic and social characteristics of the study participants

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The medical history, clinical and laboratory risk factors for stroke and kidney disease in the study participants are highlighted in [Table 3]. Self-reported history of hypertension alone was found in a significantly higher number of patients compared to controls (71.4% vs. 20.0%, P < 0.001). Following physical examination, hypertension was demonstrated in a significantly higher proportion of patients with stroke when compared to the controls (79.6% vs. 42.0%, P < 0.001). Ten patients (10.2%) had a history of smoking (with a mean duration of smoking of 12.5 years (range of 5–20 years), but none among the controls smoked (P = 0.001). The patients with stroke had a higher proportion of family history of systemic hypertension, diabetes mellitus and stroke compared with the controls. A history of excessive use of non-steroidal anti-inflammatory drugs (NSAIDs) and use of herbal medications was significantly higher in patients with stroke. A total of 90 (91.8%) patients with stroke had either no or inadequate physical activity. A total of 14 (14.3%) patients had diabetes; this was comparable to the controls which had a total of 14 (14%) participants with diabetes, however, while all the patients with stroke who had diabetes also had hypertension, this was only seen in 8 (8%) of the participants in the control group. Hypercholesterolaemia was also found in a significantly higher proportion of patients compared to controls (16.3% vs. 2.0%, P ≤ 0.001). On the other hand, there was no significant difference between both groups on assessment for hypertriglyceridaemia, abnormal high-density lipoprotein (HDL)-cholesterol and low HDL/low-density lipoprotein (LDL) ratio.
Table 3: Medical history, clinical and laboratory risk factors for stroke and kidney disease in the study participants

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[Table 4] shows the frequency of kidney dysfunction among patients with stroke and controls. Kidney dysfunction defined as as an eGFR of <60 mL/min/1.73 m2 and/or uPCR of >150 mg/g. There was a statistically higher frequency of kidney dysfunction between the patients and controls (85.7% vs. 62.0%, P < 0.001). When looking at the markers of renal dysfunction used in this study, i.e., eGFR and uPCR. There was also similarly higher proportion of individuals with uPCR >150 mg/g (75.5% vs. 32.0%, P < 0.001). However, there was a comparable proportion of patients to controls that had an eGFR of <60 ml/min/1.73 m2 (67.4% vs. 52%, P = 0.317).
Table 4: Kidney dysfunction in the study participants

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The frequency of kidney dysfunction according to stroke type using neuroimaging was determined [Table 5]. From the 76 patients who had neuroimaging, patients with haemorrhagic stroke had a statistically significant higher frequency of kidney dysfunction than those with ischaemic stroke (93.8% vs. 77.3%, P = 0.048).
Table 5: Kidney dysfunction according to stroke type in patients who had neuroimaging (n=76)

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[Table 6] shows kidney dysfunction in patients with stroke according to the severity of stroke at presentation. The mean eGFR level was significantly lower in those with severe stroke symptoms compared to those with mild stroke symptoms (42.3 ± 26.3 vs. 67.9 ± 29.6 ml/min/1.73 m2, P = 0.018). The frequency of kidney dysfunction shows a statistically significant increase with the severity of stroke. There was a higher proportion of patients with kidney dysfunction in patients with severe stroke symptoms at presentation compared to those with mild stroke symptoms (88.9% vs. 66.7%, P = 0.047).
Table 6: Renal dysfunction in patients with stroke according to the severity of stroke at presentation

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Kidney dysfunction in patients with stroke according to the severity of stroke at 7 days is shown in [Table 7]. The mean eGFR levels were significantly lower in those with severe stroke symptoms compared to those with mild stroke symptoms (6.3 ± 1.8 vs. 68.3 ± 26.9 ml/min/1.73 m2, P ≤ 0.001). The frequency of kidney dysfunction shows a statistically significant increase with the severity of stroke, with an increasing frequency seen with worsening stroke severity. A total of 70 (71.4%) patients had poor outcomes, of which 18 (25.7%) died on or before 7 days.
Table 7: Renal dysfunction in patients with stroke according to the severity of stoke after 7 days

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[Table 8] highlights the relationship between baseline characteristics and markers of kidney dysfunction and poor outcomes. The age, female gender, eGFR, serum LDL-cholesterol and HDL/LDL ratio were seen to influence outcome after 7 days in the multivariate-adjusted logistic regression analysis. A low eGFR was associated with poor outcome (OR: 0.955, 95% CI: 0.924 – 0.986, P = 0.005). Stroke severity at presentation using NIHSS, and Glasgow Coma Scale at presentation were also associated with poor outcome. uPCR, diastolic blood pressure, serum cholesterol, serum triglyceride, fasting plasma glucose and HDL-cholesterol all had no significant influence on patient outcome.
Table 8: The relationship between baseline characteristics and markers of renal dysfunction and poor outcomes

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  Discussion Top


The frequency of kidney dysfunction in patients with acute stroke in our study was 85.9%, which was comparable to the frequency of 91.0% reported by Sandsmark et al.[25] The observed frequency in this study was greater than 28.1% observed by Tsagalis et al., and 80.5% reported by Wang et al.[15],[26] The similarity to Sandsmark et al.'s study could be attributed to the racial similarities to our study.

Kidney dysfunction was significantly higher in the patients compared to the controls. A similar finding was seen in the Sandsmark et al.'s study (91.0% vs. 82.0%). The high frequency of kidney dysfunction in the patient group compared to the control group could be attributed to the higher proportion of systemic hypertension, hypercholesterolaemia, and the higher proportion of use of NSAID and herbal medications, which are all risk factors for kidney dysfunction.[27] The higher frequency of kidney dysfunction in the patients compared to the controls can be attributed more to the difference in uPCR than the eGFR. Incidentally, this observation between the proteinuria and eGFR was also seen in the study by Sandsmark et al.[25] There is a close but complex relationship between kidney dysfunction and stroke.[28] Some of these associations are attributed to the shared unique pathophysiology resulting from shared susceptibility to traditional cardiovascular risk factors.[29] Furthermore, some genetic (such as polycystic kidney) or acquired conditions (such as connective tissue disease) may cause both kidney dysfunction and stroke.[29] The kidney and the brain share unique anatomical and physiological features that render them vulnerable to conventional cardiovascular risks such as hypertension, diabetes and smoking.[28],[30]

Kidney dysfunction was significantly prevalent in patients with haemorrhagic stroke; this finding is similar to that of the Rotterdam study.[31] Our study also demonstrated that severe kidney dysfunction was commoner in patients with haemorrhagic stroke when compared to ischaemic stroke. Reports from different studies have shown divergent results on the relationship between kidney dysfunction and stroke type.[16],[31],[32] The Rotterdam study showed that decreased GFR is a strong risk factor for haemorrhagic stroke.[31] While Huang et al. and Snarska et al. reported that albuminuria was associated with a higher incidence of ischaemic stroke.[17],[18] However, in a pooled analysis by Mahmoodi et al., a low eGFR was significantly associated with increased risk of ischaemic stroke, while urine albumin, creatinine ratio (UACR) was significantly associated with both types of stroke though the risk gradient was steeper for haemorrhagic than for ischaemic stroke.[32] The relationship between kidney dysfunction and haemorrhagic stroke may be attributed to two factors: first, the nephron's small vessels, as well as those in the brain, have anatomical and functional similarities; thus small vessel disease in the brain is linked to brain haemorrhage and small vessel disease in the kidneys are similar in pathology.[33] Second, kidney dysfunction causes platelet dysfunction as a result of high urea levels.[34] Thus, the higher mean serum urea level seen in the stroke patients could explain the observed relationship between haemorrhagic stroke and kidney dysfunction seen in this study.

This study showed that the presence of kidney dysfunction was associated with the severity of stroke at presentation, and on the 7th day. The Fukuoka Stroke Registry and the China National Stroke Registry both reported a similar relationship between kidney dysfunction and stroke severity.[11],[34] Huang et al. assessed kidney dysfunction using UACR and eGFR in patients with stroke and observed high NIHSS scores at presentation.[17] The relationship between kidney dysfunction and stroke severity can be attributed to the systemic effects of azotaemia as well as the metabolic changes to phosphate and calcium metabolism.

Low eGFR was a significant predictor of poor outcome on the 7th day (OR 0.955, 95% CI 0.924 – 0.986, P = 0.005). When looking at the markers of renal dysfunction used in this study, i.e., eGFR and uPCR. Low eGFR has been reported as an independent risk factor for mortality in the general population, and in patients with CVD.[16],[30] MacWalter et al. showed that increased serum creatinine was associated with high mortality risk.[13] Yeh et al. reported that low eGFR was associated with poor outcome in patients with stroke after 6 months.[35] The China National Stroke Registry demonstrated that an eGFR of <45 mL/min/1.73 m2 was independently associated with risk of all-cause death, the combined end-point of stroke or death, and stroke disability in patients.[34] The mechanism underlying this relationship is that chronic inflammation and endothelial dysfunction in kidney disease leads to larger infarcts or more severe cerebral bleeds leading to a tendency to stroke progression.[11]

Limitations

This study did not assess the impact of other factors that may have influenced outcome directly or indirectly such as the treatment given at home or another facility before presenting at BUTH, uniformity in care/treatment (particularly taking into consideration that patients' care was funded out-of-pocket). The study also did not take into consideration other post-stroke complications that can influence outcomes such as aspiration pneumonitis, pulmonary embolism and infections.


  Conclusion Top


This observational study showed that kidney dysfunction was prevalent in patients with stroke. Urinary protein creatinine ratio defined more patients with kidney dysfunction when compared with eGFR. Patients with haemorrhagic stroke experienced kidney dysfunction more than those with ischaemic stroke. Kidney dysfunction was also associated with increasing the severity of stroke. A low eGFR was seen to be a risk factor for poor short-term outcome in stroke.

The assessment of kidney dysfunction at presentation in patients with stroke should be considered, as it predicts poor outcome. The significance of uPCR as a marker for stroke risk may warrant further investigation into the treatment of proteinuria as a strategy for stroke prevention. eGFR should be added to other known prognostic factors and emphasis should be given to the identification and management of unrecognised kidney disease in patients with stroke.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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Yeh SJ, Jeng JS, Tang SC, Liu CH, Hsu SP, Chen CH, et al. Low estimated glomerular filtration rate is associated with poor outcomes in patients who suffered a large artery atherosclerosis stroke. Atherosclerosis 2015;239:328-34.  Back to cited text no. 35
    



 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8]



 

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